{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T17:58:06Z","timestamp":1794333486648,"version":"4.9.0"},"reference-count":35,"publisher":"Informa UK Limited","issue":"1","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Computer Assisted Surgery"],"published-print":{"date-parts":[[2025,12,31]]},"DOI":"10.1080\/24699322.2025.2466426","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:46:16Z","timestamp":1740397576000},"update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study"],"prefix":"10.1080","volume":"30","author":[{"given":"Emanuele","family":"Frassini","sequence":"first","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teddy S.","family":"Vijfvinkel","sequence":"additional","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]},{"name":"Reinier de Graaf Hospital","place":["Delft, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rick M.","family":"Butler","sequence":"additional","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten","family":"van der Elst","sequence":"additional","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]},{"name":"Reinier de Graaf Hospital","place":["Delft, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benno H. W.","family":"Hendriks","sequence":"additional","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]},{"name":"Medical Systems, Philips Medical Systems","place":["Best, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John J.","family":"van den Dobbelsteen","sequence":"additional","affiliation":[{"name":"Mechanical, Maritime and Materials Engineering, Delft University of Technology","place":["Delft, The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"301","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"WHO. Cardiovascular diseases; 2021. Available from: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.carrev.2015.12.011"},{"key":"e_1_3_3_4_1","first-page":"313","volume-title":"PATAT 2018 - Proceedings of the 12th International Conference on the Practice and Theory of Automated Timetabling","author":"van Heuven van Staereling I. a.","year":"2018","unstructured":"van Heuven van Staereling I. a. Stochastic scheduling techniques for integrated optimization of catheterization laboratories and wards. PATAT 2018 - Proceedings of the 12th International Conference on the Practice and Theory of Automated Timetabling; 2018. Vienna: PATAT. p. 313\u2013329."},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0631-1"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-018-6417-4"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/CRD.0000000000000294"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2017.10.031"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/applbiosci3010002"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s004640000247"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1213\/00000539-199911000-00030"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1213\/ane.0b013e3181921c37"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-016-1474-4"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87202-1_41"},{"key":"e_1_3_3_15_1","first-page":"1718","volume-title":"Proceedings of the 20th National Conference on Innovative Applications of Artificial Intelligence - Volume 3","author":"Padoy Na-O.","year":"2008","unstructured":"Padoy Na-O. On-line recognition of surgical activity for monitoring in the operating room. Proceedings of the 20th National Conference on Innovative Applications of Artificial Intelligence - Volume 3; 2008. Chicago: AAAI Press. p. 1718\u20131724."},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2878055"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43996-4_16"},{"key":"e_1_3_3_18_1","first-page":"917","article-title":"Deep learning for time series classification: a review","author":"Hassan IF-A.","year":"2019","unstructured":"Hassan IF-A. Deep learning for time series classification: a review. Data Min Knowl Discov. 2019;33(4):917\u2013963.","journal-title":"Data Min Knowl Discov"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2020.06.008"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107322"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSSC.1969.300225"},{"key":"e_1_3_3_23_1","volume-title":"Deep learning for time-series analysis","author":"Gamboa JC.","year":"2017","unstructured":"Gamboa JC. Deep learning for time-series analysis. 2017. ArXiv, abs\/1701.01887"},{"key":"e_1_3_3_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"e_1_3_3_25_1","first-page":"1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Szegedy C. a.","year":"2015","unstructured":"Szegedy C. a. IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Going deeper with convolutions, Boston, MA, USA; 2015. p. 1\u20139."},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.014"},{"key":"e_1_3_3_27_1","volume-title":"Conference on Computer Vision and Pattern Recognition","author":"Wu JH.","year":"2019","unstructured":"Wu JH. Conference on Computer Vision and Pattern Recognition; 2019. Squeeze-and-Excitation Networks, Salt Lake City, Utah."},{"key":"e_1_3_3_28_1","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A. a.","year":"2017","unstructured":"Vaswani A. a. Advances in Neural Information Processing Systems; 2017. Attention Is All You Need. Curran Associates, Inc. (30)"},{"key":"e_1_3_3_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/biology12071033"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"e_1_3_3_31_1","first-page":"211","volume-title":"Proceedings of the 2nd International Conference on Neural Information Processing Systems","author":"Bridle JS.","year":"1989","unstructured":"Bridle JS. Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. Proceedings of the 2nd International Conference on Neural Information Processing Systems; Morgan-Kaufmann; 1989. p. 211\u2013217."},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05815-0"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1569901.1570067"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(92)90008-W"},{"key":"e_1_3_3_35_1","volume-title":"3rd International Conference for Learning Representations","author":"Ba DP.","year":"2015","unstructured":"Ba DP. Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations; San Diego, 2015. Ithaca, NY: ArXiv."},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103126"}],"container-title":["Computer Assisted Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/24699322.2025.2466426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T07:01:49Z","timestamp":1766473309000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/24699322.2025.2466426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12,31]]}},"alternative-id":["10.1080\/24699322.2025.2466426"],"URL":"https:\/\/doi.org\/10.1080\/24699322.2025.2466426","relation":{},"ISSN":["2469-9322"],"issn-type":[{"value":"2469-9322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=icsu21","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=icsu21","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-02-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2466426"}}